To address the issues that oil reservoir geological data are complex to represent and the existing methods struggle to fully extract the features of oil reservoir sweet spots， an oil reservoir sweet spot identification method based on Shapelet-Transformer was proposed. In this method， well log data were re-represented as a multi-variable depth sequence and a network model integrating local and global features was constructed to achieve accurate identification of oil reservoir sweet spots. First， a local feature learning unit was proposed， and the unique local well log response features of sweet spots were captured through the Shapelet extraction and selection mechanism. Second， a global feature learning mechanism was constructed to mine the global trend and parameter correlation of well log sequences， thereby providing global geological features. Finally， a fusion and classification module was designed to output the identification results of oil reservoir sweet spots by fusing local and global features. Experimental results show that the average identification accuracy of this method on the well log data of Ma 2 well block is 84.62 %， which is at least 1.26 percentage points higher than that of Fully Convolutional Neural Network （FCNN）， BiLSTM-FCNN （Bidirectional Long Short-Term Memory and FCNN hybrid model）， Temporal Graph Convolutional Network （T-GCN）， Disaggregated Multi-Scale Time Series decomposition model （DisMS-TS） and other benchmark models， which verifies the effectiveness of this method.